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Martre, P.* ; Wallach, D.* ; Asseng, S.* ; Ewert, F.* ; Jones, J.W.* ; Rötter, R.P.* ; Boote, K.J.* ; Ruane, A.C.* ; Thorburn, P.J.* ; Cammarano, D.* ; Hatfield, J.L.* ; Rosenzweig, C.* ; Aggarwal, P.K.* ; Angulo, C.* ; Basso, B.* ; Bertuzzi, P.* ; Biernath, C.J. ; Brisson, N.* ; Challinor, A.J.* ; Doltra, J.* ; Gayler, S.* ; Goldberg, R.* ; Grant, R.F.* ; Heng, L.* ; Hooker, J.* ; Hunt, L.A.* ; Ingwersen, J.* ; Izaurralde, R.C.* ; Kersebaum, K.C.* ; Müller, C.* ; Kumar, S.N.* ; Nendel, C.* ; O'Leary, G.* ; Olesen, J.E.* ; Osborne, T.M.* ; Palosuo, T.* ; Priesack, E. ; Ripoche, D.* ; Semenov, M.A.* ; Shcherbak, I.* ; Steduto, P.* ; Stöckle, C.O.* ; Stratonovitch, P.* ; Streck,T.* ; Supit, I.* ; Tao, F.* ; Travasso, M.* ; Waha, K.* ; White, J.W.* ; Wolf, J.*

Multimodel ensembles of wheat growth: Many models are better than one.

Glob. Change Biol. 21, 911-925 (2015)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Ecophysiological Model ; Ensemble Modeling ; Model Intercomparison ; Process-based Model ; Uncertainty ; Wheat (triticum Aestivum L.); Climate-change; Crop Production; Impacts; Yield; Simulations; Calibration; Australia; Billion; Europe; Grain
Sprache englisch
Veröffentlichungsjahr 2015
HGF-Berichtsjahr 2015
ISSN (print) / ISBN 1354-1013
e-ISSN 1365-2486
Zeitschrift Global Change Biology
Quellenangaben Band: 21, Heft: 2, Seiten: 911-925 Artikelnummer: , Supplement: ,
Verlag Wiley
Verlagsort Hoboken
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Soil Ecology (IBOE)
POF Topic(s) 20405 - Terrestrial Systems – from Observation to Prediction
Forschungsfeld(er) Environmental Sciences
PSP-Element(e) G-504400-003
PubMed ID 25330243
DOI 10.1111/gcb.12768
WOS ID WOS:000348652400033
Scopus ID 84923027677
Scopus ID 84919663286
Erfassungsdatum 2015-01-01
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